On-line measuring system and method

ABSTRACT

The invention relates to a system for the on-line measurement of a parameter in a processing stream. The system comprises a scanning head mounted adjacent a continuous stream of processed material, the scanning head comprising a remote light source and reflected light gathering and transmission apparatus; a near infrared spectrophotometer which includes a monochromator for resolving the reflected light into light of a discrete wavelength; a database containing a reference calibration equation linking absorption characteristics by wavelength and the quantified presence of the parameter of interest; and a computer for measuring the parameter by application of the calibration equation to the obtained spectrum for a sample and managing said system. The invention also comprises a method of on-line measurement of a parameter in a processing stream by NIR spectroscopy and networks comprising systems according to the invention. The system and method of the invention are particularly suited to measuring a parameter of interest in processed sugar cane.

This invention relates to a system and method for measuring a parameterof interest in a processing stream. In particular, the invention relatesto the use of near infrared spectroscopy for the on-line measurement ofa parameter of interest in a processing stream. More particularly, theinvention relates to the use of near infrared spectroscopy for theon-line measurement of parameters of interest in sugar cane processing.

BACKGROUND ART

Processing of biological material such as plant material usuallyinvolves measuring parameters of interest in the starting material andthrough to the desired end product or products. The measurement of theparameters can be for process control or for monitoring the level of acomponent in the material.

Measurements are typically made on samples taken from the processingstream. Depending on the parameter being measured, there can be a delayin determining the value of a parameter, as a consequence of which theparameter is not determined in real time. Furthermore, a sample may notbe truly representative of the bulk of the material being processed atthe point of sampling.

There can be other difficulties with measuring parameters of interest ina processing stream by sampling. A particular field where suchdifficulties occur is sugar cane processing. Sugar and related productsare provided by processing sugar cane in sugar mills where the cane iscrushed and processed through to crystalline raw sugar and molasses. Thecane is supplied by plantation owners or individual cane farmers withthe owners or farmers being paid for the cane supplied on the basis oftonnage and quality.

In Australia, for example, the amount paid to a plantation owner orfarmer for each parcel, of the supplied cane is determined by weighingthe parcel and evaluating the sugar content of the cane. Anindustry-agreed value is then used to calculate the amount paid for theparcel, the whole process being referred to as the “cane paymentscheme”. Various systems are used to determine the sugar content of thesupplied cane. Each of the systems requires sampling, sample processingand analysis. In many countries, a core of cane is withdrawn from thedelivered cane, processed by pressing or wet disintegration to juicewhich is analysed and converted to a measure of the sugar content of thecane.

The system used in Australia requires analysis of the earliest juice(first expressed juice) driven out of the cane crushing rollers. Thisentity has been shown to be convertible to and representative of theanalysis of the whole cane. The cane payment system requires continuoussampling of the first expressed juice throughout the period of crushingeach parcel of cane and an analysis reported for that parcel. Payment isbased on this analysis and the weight of the parcel. The juice issubsampled and analysed according to standard proscribed methodologiesfor “pol” (a measure of the sucrose content of the juice) and “brix” (ameasure of the dissolved solids content of the juice). The estimation ofthe total cane supply is determined by means of the first expressedjuice analysis, the fiber content of the cane and empirically determinedrelationships linking the juice analysis and total cane analysis.

The fiber analysis of the cane for which the juice is sampled isdeterminable by washing a representative sample of the cane free ofdissolved solids. The sample is prepared by cutter grinding to anappropriate fineness prior to washing. Sampling and analysis forindividual parcels is not practical and a deemed fiber is used incalculations on an individual farmer's cane delivery.

Each variety of cane is allocated to one of two or three classes of moreor less common average fiber composition. The deemed or “class fiber” asit is termed is a rolling average of the fiber of the class which hasbeen allocated to the cane variety. The rolling average is obtained byregular sampling of each class throughout the day, compositing withinthe class and determination of the daily average fiber. Several daysanalyses are combined in the rolling average.

The use of class fiber is a particular weakness of the current systembut is forced upon the system by financial and practical impossibilityin providing a representative, meaningful individual fiber compositionfor each parcel of cane. Providing such representative and meaningfuldata in a typical Australian sugar mill would require the analysis of atleast 150 parcels of cane each day, in duplicate, requiring a samplingteam of approximately 30 persons, approximately 20 fiber analysinginstruments and 4 to 5 cutter grinding machines.

The class fiber system also makes no distinction between the actualcondition of the particular parcel of harvested cane in respect of dirtand extraneous matter content or in respect of inherent differences thatmay pertain to its plant or ratoon status.

Cane analysis is labour intensive and relies on extended samplingprocedures, analysis and subsequent conversion to an estimate of theanalysis of the whole cane received. The methodology in practical usedoes not give adequate feedback to the grower on cane quality oradequate feed forward for process control to the miller as it does notuse individual parcel fiber analyses in the compositions.

Spectroscopy is a technique whereby a chemical compound can beidentified by the degree of modification to light at differentfrequencies or wavelengths. Near infrared spectroscopy (NIR)—that is,spectroscopy where absorption of light over the range 400 to 2500 nm isanalysed—has been previously applied as a tool in such fields as themeasurement of protein and moisture in grain, the composition of foragefor animal food, the degree of ripeness of fruit and the composition offine cane particles, cane juices, syrups and sugars in sugar laboratorysituations. However, NIR has not previously been exploited on-line formeasuring parameters of interest in a processing stream such ascomminuted sugar cane during cane processing.

SUMMARY OF THE INVENTION

The object of the invention is to provide a system and method for theon-line measurement of parameters of interest in a processing stream,which system and method utilise near infrared spectroscopy. A particularobject of the invention is to provide a system and method for theon-line measurement of parameters of interest in sugar cane duringprocessing of the cane utilising near infrared spectroscopy.

According to a first embodiment of the invention, there is provided asystem for the on-line measurement of a parameter in a processingstream, the system comprising:

(a) a scanning head mounted adjacent a continuous stream of processedmaterial, the scanning head comprising a remote light source andreflected light gathering and transmission apparatus;

(b) a near infrared spectrophotometer which includes a monochromator forresolving the reflected light into light of a discrete wavelength;

(c) a database containing a reference calibration equation linkingabsorption characteristics by wavelength and the quantified presence ofthe parameter of interest; and

(d) a computer for measuring the parameter by application of thecalibration equation to the obtained spectrum for a sample and managingsaid system.

According to a second aspect of the invention, there is provided amethod of on-line measurement of a parameter in a processing stream, themethod comprising the steps of:

(i) obtaining an infrared reflectance spectrum from a stream ofprocessed material;

(ii) applying an appropriate calibration equation to the spectrum toquantify the presence of the parameter of interest; and

(iii) statistically validating the spectrum obtained as beingrepresented by the calibration equation.

According to a third embodiment of the invention, there is provided asystem for the on-line measurement of a parameter in processed sugarcane, the system comprising:

(a) a scanning head mounted adjacent a continuous stream of processedcane, the scanning head comprising a remote light source and reflectedlight gathering and transmission apparatus;

(b) a near infrared spectrophotometer which includes a monochromator forresolving the reflected light into light of a discrete wavelength;

(c) a database containing a reference calibration equation linkingabsorption characteristics by wavelength and the quantified presence ofthe parameter of interest; and

(d) a computer for measuring the parameter by application of thecalibration equation to the obtained spectrum for a sample and managingsaid system.

According to a fourth aspect of the invention, there is provided amethod of on-line measurement of a parameter in processed sugar cane,the method comprising the steps of:

(i) obtaining an infrared reflectance spectrum from a stream of saidprocessed cane;

(ii) applying an appropriate calibration equation to the spectrum toquantify the presence of the parameter of interest; and

(iii) statistically validating the spectrum obtained as beingrepresented by the calibration equation.

The term “on-line” as used in the above definitions of embodiments andhereafter denotes measurement at the actual process stream as well asmeasurement on a portion of the stream of material being processedthrough a by-pass line. The second of the meanings given in thepreceding sentence is commonly referred to as an “at-line” measurement.

The term “processing stream” as used in the definitions of the first andsecond embodiments of the invention denotes a stream of material derivedfrom plants wherein the plant, or a part thereof, has been comminuted.Alternatively, plant-derived material can be material that has beenextracted from a plant, or part thereof, by procedures such as slicing,dicing, shredding, mincing, pulping, pressing, sawing or rasping. Theprocessing stream referred to in these embodiments thus includes, but isnot limited to, prepared or comminuted sugar cane, sliced sugar beet,crystal sugar, bagasse at various stages, silage and processed grains,fruit and vegetables as well as processed fruit and vegetables, particleboard and paper.

It will be appreciated from the foregoing description of the third andfourth embodiments that the invention allows on-line assessment of canequality providing, in contrast to present measurement systems,meaningful fully representative information. Parameters of interestwhich can be measured using the system and method of the inventioninclude fiber content, juice brix or dissolved solids content, juicepolarisation or sugar content, commercial sugar content or CCS of cane,water, and other quality parameters such as ash which is related to dirtcontent, individual inorganic elements, and process parameters such aspol in open cells. The inorganic parameters that can be measured includethe following: phosphate; nitrogen; calcium; magnesium; potassium; iron;and, silicon. Extraneous matter such as dirt, tops, trash and suckerscan also be measured.

The term “processed sugar cane” or derivatives thereof as used hereininclude within their scope, prepared cane, intermediate and finalcrushing roller bagasse, boiler feed materials, raw sugar andcrystalline sugar. Consequently, in addition to the parameters givenabove, other parameters that can be measured include pol, moisture,grist, filterability, starch, dextran and other polysaccharides.

The continuous stream of processed cane can be a stream included in anormal sugar milling process or can be a stream set up for analyticalpurposes. In other words, the invention is not restricted in applicationto the milling process per se. An example of a stream set up foranalytical purposes is processed cane from core samples of cane batches,which samples are processed in a mill's analytical laboratory.

A requirement of the stream, however, is that the material be devoid ofgross voids so that the scanning head sees an essentially unbroken layerof the processed cane. If necessary, the conduit or the like carryingthe stream can be constricted to effect a compression of the materialpassing the scanning head.

In the system and method of the invention, the scanning head is mountedat a fixed distance from the surface of the processing stream, which, inthe case of the third and fourth embodiments, is a stream of processedcane. The distance between the surface of the processing stream and thescanning head is usually fixed within the range of 75 to 100 mm with thedistance maintained at ±5 mm. Maintenance of a set distance is necessaryfor accurate application of calibration equations to parametermeasurement.

As an example of the mounting of the scanning head in sugar caneprocessing, the head is mounted at the feed chute for the first crushingmill of a raw sugar mill. Other suitable positions for mounting thescanning head on equipment included in a sugar mill will be detailedbelow.

A suitable cradle may be required for mounting the scanning head toinsulate the head from vibration present in the equipment comprising theprocess. This is particularly the case with sugar milling equipment.Temperature control is also advantageous such as by mounting thescanning head in an air-conditioned chamber. As a window has to beprovided for light transfer between the scanning head and the processedcane, the sensing head cradle preferably allows repositioning of thesensing head so that the window glass can be removed for cleaning orreplacement. Cleaning or replacement of the window glass is necessary asdebris from a processing stream such as processed cane passing thewindow can build up on the glass or damage the glass, interfering withoperation of the system.

The spectrophotometer is preferably remote from the scanning head butlinked thereto by a fiber optic cable. The spectrophotometer, like thesensing head, is vibration and temperature insulated if necessary. Whenremotely located, the requirements of the vibration insulation means inrespect of the spectrophotometer are not as stringent as for the sensinghead. However, the sensing head and spectrophotometer can be an integralunit in which case the vibration and temperature insulation must meetthe requirements of the sensing head. Any insulated chamber housing thespectrophotometer is advantageously airconditioned.

The spectrophotometer can be any suitable commercially availableinstrument. An example of a suitable instrument is Foss NIRSystems Model6500 system incorporating a Model 6500 monochromator, Direct LightReflectance System and ISI NIRS3 or Vision Software supplied by FossNIRSystems Inc, 121021 Tech Rd, Silver Spring, Md. 20904, USA. Thisinstrument has a scanning range of 400 to 2,500 nm. However, anothersuitable instrument is the NIRSystems Model 5000 which operates over awavelength range of 1,100 to 2,500 nm.

The database calibration equations referred to in section (c) of theabove definitions of the first and third embodiments can be determinedby the gathering of reflectance spectra on the material present in theprocessing stream and statistically evaluating the data using a routinelaboratory assay of the parameters of interest on concomitant samples ofthe material. A minimum of 200 assay samples are generally required forequation development and inclusion in the database. The interfacedcomputer referred to in section (d) of the first and third embodimentsserves to link the analytical system with the individual user of thedata or the dependent processes. In the case of prepared cane, it isnecessary that the instrument be linked to the cane sample trackingsystem to provide cane parcel identification for the cane being scannedand to have the capability of inserting the result of the computationsfor parameters of interest into the files in the cane payment system forthe relevant parcel of cane. The computer software advantageously hasthe capability of delivering analytical data to process controllers inreal time.

The calibration equations are crucial to the operation of the system andthe method but can nevertheless be developed by one of ordinary skill inthe art, particularly with the guidance of the NIR instrumentmanufacturer. The process involves the steps of collection of spectraland laboratory data, population structuring, calibration development andfinally, validation of the equation. It is essential that the linkbetween the spectral data and the corresponding laboratory analysis isstrong. Using sugar cane processing as an example, the spectral data iscollected by scanning the portion of cane as it passes the read head,collecting all spectra associated with that portion and averaging themto produce a single spectral result for that portion. The correspondinglaboratory sample, in the case of calibration for fiber, is obtained bytaking small snap samples from the process stream over the whole lengthof the portion. These snaps are thoroughly mixed to produce an averagelaboratory sample, which is then sub-sampled for analysis by alaboratory can fiber machine or a laboratory bag fiber analysisprocedure. A minimum of 200 such pairing of analyses of portions of caneare required to produce a preliminary equation. To produce a robustglobal equation, cane should be sampled which contains as much aspossible of the likely variation in cane. The calibration software isused to determine the population boundaries of the calibration set whichis necessary to define the spectra which are represented by thecalibration equation to be developed. Spectra three standard deviationsfrom the mean spectral result are discarded. The software selects thecalibration set and the validation set from the spectra and theirlaboratory results. The calibration equation is developed from thecalibration set using, for example, partial least squares calibrationmathematics. The equations are validated, firstly, by applying theequation to the validation set and then in an on-line situation withspectra that were in no way associated with the calibration process.

A “local” calibration technique can also be employed. This calibrationtechnique uses the library developed in the population structuring stepin an on-line situation to select similar spectral results to an unknownspectrum obtained from a scan. These spectra and results are used todevelop an equation which is then applied to the unknown spectrum. Thisapproach is particularly effective in a system of networkedspectrophotometers employing the advantages in robustness of the globalcalibration with the precision of a local calibration.

As indicated above in the definition of the second and fourthembodiments, a preliminary step in the method of the invention is theobtainment of an infrared reflectance spectrum of the processing streamcontaining the parameter of interest. An objective in this step is tominimise the time taken to complete a scan so that more scans which are,in effect, sub-samples can be taken during the measurement of aparticular parameter during the processing of a portion of material ofinterest.

Using measurement of a parameter in processed sugar cane as an example,scanning of a sample is initiated on receipt of instructions from acomputer controlling the scanning operation, which computer is alsocentral to the execution of the method. The term “sample” in theforegoing context denotes the measurement of a parameter in a particularportion of the processed material and in determining parameters incrushed cane, this portion will be what is referred to as a “parcel” ora “rake” of cane. In a typical scan of about 36 seconds duration, about26 seconds is taken up in acquiring up to 32 full spectral passes and 9to 10 seconds for signal transfer. Scan frequency can be increased toallow more time for scanning the process material. The signal to noiseratio is optimised by assessing individual scans and corrupted scansdiscounted. Corruption, for example, can be due to voids in the crushedcane passing the scanning head.

Depending on the time taken for a parcel to pass the scanning head, anyscans obtained can be computed with the calibration equations and theresults averaged to give a representative parameter value for theparcel.

The computer referred to in sub-paragraph (d) of the definition of thefirst and second embodiments can also serve to manage signals receivedthereby and in presentation of appropriate information from thedatabase. It will be appreciated that normally there would be interestin more than one parameter in the processing stream. Consequently, thedatabase must hold a reference calibration equation for each of thoseparameters. For example, in sugar cane processing, % fiber, brix and %water are measured in the initial crushed cane. Spectral integrity ischecked and the validity of predicted results can be assessed bydetermining that the sum of these values is 100±5. The computer, throughits control program, liaises with the mill's cane payment and processcontrol computers taking current information and monitoring scanningconditions. It initiates a scan through the scan program when conditionsare conducive. The scan program starts the instrument scan and receivesthe spectrum when the scan is completed. The scan program is used toapply the calibration equation for the component of interest tocalculate a result from the spectrum which is passed to the controlsoftware with its evaluation of the conformity of the spectrum obtainedto the spectra in the calibration set. The control software inspects andvalidates the results. If the result is accepted, it updates processcontrol signals and cane payment details, computing averages for therake when the rake ends and passing average results from accepted scansto the cane payment computer.

In the method of the second and fourth embodiments of the invention,step (iii) can additionally comprise calling for physical samplingshould a measured spectrum lie outside the range predicted by acalibration equation. That is, if an unusual spectrum is obtained, asample can be obtained for measurement of the parameter of interest byroutine analytical procedures.

The system according to the first and third embodiments of the inventioncan be incorporated into a network. In such a network, a centraliseddatabase provides reference calibration equations to other processingstreams in the network. Further, provided that instruments arestandardised against the instrument on which the calibration equationswere developed, the latter instrument can be used as a master instrumentwithin the network. Global reference calibration equations provide acalibration which is robust to changes in processing streamcharacteristics, avoiding taking additional calibration samples and is afeature of the network arrangement.

A network of systems according to the invention is particularlyadvantageous in measuring parameter of interest in sugar caneprocessing. In this industry, sugar mills in particular region may bepart of a network. Application to the network of a system according tothe third embodiment facilitates operation of the mills, with fullyinterchangeable instruments, in such a manner as to use commoncalibration equations or allow access to a suitable equation from thenetwork to apply to cane with unfamiliar characteristics and isparticularly advantageous for the cane payment system discussed above.

Having broadly described the invention, the system and method will nowbe exemplified with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of a system according to theinvention applied to the measurement of parameters in sugar caneprocessing.

FIG. 2 is a schematic diagram of a sugar mill showing where parametersof processed sugar cane can be measured during processing of sugar caneand sugar.

FIG. 3 is a schematic showing the positioning of a scanning head withrespect to a chute supplying cane to crushing rollers.

FIG. 4 is a perspective view of a cradle for the NIR spectrophotometer.

FIG. 5 is a perspective view of a cradle for the scanning head.

FIG. 6 is a cross sectional view of a housing for the scanning head.

BEST MODE FOR CARRYING OUT THE INVENTION

FIG. 1 is a schematic drawing of the system. The scanning head of thedirect light reflectance system of a NIR Monochromator class ofspectrophotometer is positioned alongside of or above a process streamto be analysed. Reflected light passes by way of fiber optics to thespectrophotometer where the light is broken into wavelengths over therange 400 to 2500 nm in steps of typically 2 nm. A spectrum of theabsorption by the process stream, by wavelength, is produced for eachscan of the sample. A database of calibration equations is stored foreach parameter of interest such as fiber content in the process stream.This information is held available for access by the CPU. The databasealso stores the characteristics of the spectra used in deriving thecalibration equations. An average spectrum is produced for each samplescan. The relevant sections of the spectrum for the calibration ofinterest are extracted and computed to deliver the measured parameterfor the scan. The results from all the accepted scans of the relevantportion of the process stream are averaged for the prediction. Thespectrum obtained is useable for as many parameters as calibrations areavailable. The CPU can reject a spectrum that does not conform to theset of spectra used to derive the calibration equation.

Turning to FIG. 2, there is schematically shown portions of a sugarmilling process. In the figure, a hammer mill shredder 1 is shownfollowed by a series of crushing rollers, 2 to 7. Bagasse—residue canefiber—is taken away from the final crushing roller 7 by conveyor 8 to beused, for example, as fuel in the mill power house 9. Cane juice fromthe milling train 10 is further processed through to final steps whichinclude centrifugation 11 and drying 12 from which raw sugar istransported to a storage bin 13.

Parameters of interest can be measured using the system of the inventionwith the scanning head installed in the following process streams: atthe beginning 14 or end 15 of the feed chute from hammer mill 1 to thefirst set of crushing rollers 2; at the beginning 16 or end 17 of feedchutes between successive sets of crushing rollers such as indicatedbetween crushing rollers 5 and 6; the conveyor 8 which transportsbagasse away from milling train 10; the line 18 carrying crystallinesugar from centrifugal 11 to drier 12; and, the conveyor 19 carryingdried sugar to storage bin 13.

FIG. 3 is a schematic representation of the mounting of a scanning head20 to a feed chute 21 supplying crushed cane 22 to first pressure feederrollers 23 and mill rollers 24 which collectively form one of the setsof crushing rollers referred to above in connection with FIG. 2.Scanning head 20 is mounted to a wall 25 of feed chute 21 with a viewingwindow 26 between the head and the interior of the chute. Viewing window26 is demountable for replacement or cleaning. A fiber optic cable 27transmits light from head 20 to a spectrophotometer not shown in thedrawing. The mounting of head 20 to chute wall 25 is such that thedistance between the head and the interior of the chute is fixed.

FIG. 4 shows a cradle for the NIR spectrophotometer of the system shownin FIG. 1, which cradle minimises transmission of vibration from millequipment to the NIR instrument. The cradle 50 includes a hangingbracket 51 and an instrument mounting bracket 52. Hanging bracket 51 issuspended from any suitable overhead structure by a plurality of cablesand springs, one such combination being indicated at 53 and 54,respectively. Each cable terminates with a hook, one of which isindicated at 55, for ease of demounting the cradle. High frequencydampers are provided, one being indicated at 56, for connectinginstrument bracket 52 to hanging bracket 51. Four dampers are typicallyused. A suitable type of damper is a model WR5-400-10-S-M obtainablefrom Hawker Richardson Limited of Garden Square, Macgregor Road, MtGravaft, Queensland, Australia.

A scanning head cradle is shown in FIG. 5. The cradle permits efficientdisplacement of the scanning head (in its housing—see below) forcleaning or maintenance of the window glass while allowing precisereturn of the scanning head to its scanning position.

FIG. 5 shows cradle 60 comprising hanging bracket 61, a channel 62 for atrolley assembly 63, a plurality of hanger rods one of which isindicated at 64, toggle clamps 65, and a housing 66 for the scanninghead. Channel 62 extends from the wall of the conduit for processed canewith which the scanning head is associated. The trolley assembly 63allows the cradle to be moved away from the wall once clamp 65 has beenreleased. The hanger rods are pivotally connected to hanging bracket 61so that housing 66 can be pivoted once the cradle has been moved awayfrom the conduit wall. In an alternative arrangement, the components ofFIG. 5 can be provided with a sealed controlled environment enclosure(not shown) mounted on the chute.

Housing 66 is shown in more detail in FIG. 6. The housing is essentiallya box having an open face 67 which abuts the wall of the conduit in thearea which includes the window. Housing 66 includes six low frequencydampers (model WR2-800 from Hawker Richardson) which support a mountingbracket 68. One such low frequency damper is indicated at 69. Thescanning head assembly 70 is mounted to bracket 68 via two highfrequency dampers (WR4-400-10-S-M, again from Hawker Richardson), one ofwhich is indicated at 71. Selection of dampers is made according to theweight of the piece to be protected and the vibration experienced. Aspring can be provided between the top of housing 66 and a base plate atthe bottom end of mounting bracket 68. This spring shifts the loadingbetween the top and bottom low frequency dampers. Alternatively, variousthickness insertion rubbers can be placed under the bottom end of themounting bracket for the same purpose.

The hanging bracket of the cradle described in FIG. 5 is not essential,it will be appreciated, and housing 66 can be mounted directly to a wallof the conduit for processed cane provided that appropriate vibrationdampening is used in the mounting. The mounting must of course be suchthat demounting of the housing is possible to allow access to the windowin the conduit wall.

Examples of the application of the system and method to the measurementof cane quality parameters follow.

EXAMPLE 1 Measurement of Cane Quality Parameters

In this example, we describe the development of calibration equationsfor parameters of interest in prepared cane and in raw sugar. Theexample also verifies the accuracy of the method and verifies that themethod is suitable for on-line factory use.

In this work, a process stream was sampled concurrently with scanning ofthe stream using the near infrared 6500 system referred to above. Thesugar mill's normal cane tracking system was used to follow the canethrough the process, identifying the cane at the scanning window andensuring sample correspondence at the prepared cane and first expressedcane juice sampling positions.

First expressed juice was sampled continuously over the period ofcrushing of the cane parcel under study. This juice was analysed byroutine laboratory methods for the entities of the sugar industry, brix,and polarisation, and for additional substances such as nitrogen,phosphorus, alcohols and so forth.

The prepared cane was sampled over a concomitant period to the juicesample by means of the taking as many “snap” samples as possible duringthe passage of a parcel of cane from a purpose built hatch in theducting for the process stream. As indicated, the time relativity of thesampling positions was maintained by the cane tracking system, but,typically, the fiber hatch was adjacent the near infrared scanningposition to simplify management of the process. A grab sample may betaken and unused material cleared away in 30 second cycles.Consequently, a parcel requiring 20 minutes for crushing was sampled by40 snaps into a suitably sized bin. The bin was sealed until furtherprocessing, which processing was generally completed within one hour.

The prepared cane sample was mixed and cutter ground to a samplesuitable for analysis. Mixing was accomplished by hand. In thisoperation, the bulk sample was split into two heaps and handfuls fromeach heap were intimately mixed, during which process lumps were brokenup, into a common mass. The heap so obtained was rotated 900, split andremixed and then rotated, split and further mixed. The resultant masswas then spread thinly to a thickness of about 2 cm and approximately 3kg of the mixture was randomly taken for cutter grinding. In this step,care was taken to sub-sample through to the bottom of the layer. Cuttinggrinding was achieved using one the standard devices of the Australianindustry, the JEFFCO Cutter Grinder supplied by Jeffress Engineering ofNorthgate, Brisbane, Queensland.

The cutter ground material was again thoroughly mixed by the sequencedescribed for prepared cane. Sub-samples were taken for duplicate fibercontent and duplicate moisture determination in accordance with standardindustry methods. Both bag and can fibers were obtained.

The process stream was scanned through a scanning window by means of anear infrared spectrophotometer in remote reflectance mode. Some of theabsorption spectra were taken in cycles of 1 minute 18 seconds. In thistime, the instrument completed and averaged 32 full spectral passes ofthe sample (which was travelling past the sensor at approximately 0.5m/sec) and completed and averaged 32 full spectral passes of a ceramicreference tile, then processed and transmitted the data. The remainingdata was obtained at the rate of 36 seconds for 32 full passes.

The transmitted spectrum for a scan was obtained by subtracting theaveraged reference scan from the averaged sample scan. The referencescan helped account for environmentally induced noise and confirmed theinstrument was maintaining normal operation.

The spectrum related absorption (defined as log₁₀ (1/reflectance) ) touser selected wavelength bands drawn from the entire range 400 to 2500nm. The absorption was resolved into 2 nm increments.

In calibration mode, the spectral scans which were taken during thepassage of the calibration sample were averaged and stored together withthe routine laboratory analyses for the parameters of interest. Theassembled calibration samples provided a suite of paired spectral linesand analyses. ISI, NSAS or Vision software, provided by the instrumentmanufacturers, Foss NlRSystems, was used to process the data tocalibration. ISI has predominated in this work. ISI uses the firstderivative with respect to wavelength of the average spectral line andequivalent average of routine analysis for the sample to build up acalibration equation over all the calibration samples. ISI uses amodified partial least squares (PLS) analysis resulting in a linearequation with coefficients on wavelength in 2 nm steps. The techniquebuilds an equation for the component in question and the spectra may bereused for derivation of a calibration equation for another component ofthe same sample set. The technique is also referred to as principalcomponent analysis. For cane payment purposes, many thousands of assayshave been conducted to enhance the reliability of the result predictionequations.

The calibration equation in two nm wavelength steps was used to predicta component's presence from the spectrum of an unknown passing thesampling point.

The software has the capability of determining when the spectral resultlies within a population known to give accurate predictions. Thiscapability was used at the calibration stage to identify “deformed”spectra prompting a check as to whether the deformity was due toexcessive voids in the sample at the time, whereupon the spectrum wasremoved from consideration, or that it was outside the known populationand needed to be analysed conventionally for inclusion.

In an on-line situation, the software can call for a sample to be takenif a spectrum lies outside expectations. The incorporation or not ofthis sample in the calibration will be post processed on the basis ofinspection of the process and the spectrum.

A calibration equation was derived and thereafter sampling was continuedto fill in gaps in the spectral coverage and expand the range of routinelaboratory values in the calibration equation. The following tablesprovide statistical data for the routine laboratory and NIR analyses ofcane quality parameters. The data show that the technique of NIRspectroscopy may be successfully used, on-line, in the generation ofcalibration equations that are robust and result in representativeanalyses of the cane quality parameters.

Table 1 provides a reference with which to compare the NIR results. Itpresents the error statistics for the routine laboratory analyses ofcane quality parameters. The tables which follow present the statisticalcharacteristics of the calibration equations developed, theirapplication generally, and in a particular mill. In these tables,standard errors were calculated using the standard deviation of thedifference between results.

Table 2 refers to the “global equation” developed in the experimentationin a number of mills over a number of crushing seasons using threeNIRSystems 6500 instruments. The global equation is a generic equationto be applied in any milling situation requiring minimal calibrationdevelopment. A mill-particular calibration would require significantcalibration development on first use and most likely with each change ofcane characteristic due to seasonal or growing conditions. In Table 2,the performance of the equation is shown pooled over all the mills usedin calibration development. Both the calibration statistics and thestatistics arising from on-line use (validation) are given in the table.

Tables 3 to 7 show the use of the global equation in each of the millsfor which calibration data was provided. The robustness of the equationsare highlighted by the consistency in prediction performance from onemill to the next.

Table 8 presents the statistics for useful calibration equations derivedat one mill but not yet extended to other mills or in numbers sufficientfor the calibration equation to be classified as global.

Overall, the results show a strong agreement between a NIR predictionand the result obtained by routine laboratory means.

The sugar industry's analytical measure, Commercial Cane Sugar (CCS) isderived in current practice by an empirical formula linking the canefiber content and the analysis of the first juice expressed from thecane. The invention may be used to predict the CCS directly, if desired,and this is the analytical result designated “Individual CCS”. The errorassociated with the normal computation of CCS is not known. However,extrapolation from errors in the fiber and juice analyses techniqueswould lead to a best expected laboratory standard error of approximately0.33. In practice however, the reported CCS is derived from an assumedfiber estimation which may be substantially incorrect and actual, inuse, errors will be substantially higher than 0.33. It would besubstantially higher than that which would be obtained from theIndividual CCS calibration equation or from near infrared spectroscopyprediction of fiber and calculation of CCS in the normal manner.

The near infrared method as exemplified is thus, because of its on-linecapability, substantially more reliable than is possible with currentpractice.

TABLE 1 Laboratory Duplicate Statistics for Prepared Cane samples UsedIn NIR Calibration Standard Correlation Range Number of ConstituentError Co-efficient Slope Bias (%) Samples Can Fibre 0.22 0.984 1.00 0.0011.9-23.6 1226 Combustion Ash 0.06 0.998 1.00 0.00  0.50-10.50 1400 DryMatter 0.24 0.987 1.00 −0.01 23.3-38.4 1145 Pol in open cells 1.71 0.8730.97 0.00 75.8-95.0 116 Nitrogen in Juice (mg/L) 29 0.85 0.95 0 188-61533 Phosphate in Juice (mg/L) 15 0.975 0.976 2.3 23.8-354  565 Potassium% cane 0.006 0.973 1.02 −0.002 0.07-0.22 27 Calcium % cane 0.003 0.8691.01 0.003 0.01-0.04 27 Silica % cane 0.04 0.998 0.996 −0.01 0.22-3.4 27 Magnesium % cane 0.002 0.914 0.978 −0.001 0.01-0.03 27 Insoluble Ash0.1 0.992 1.016 0 0.51-5.43 91

TABLE 2 Global NIR Equation Statistics for Prepared Cane in Calibrationand Validation for Pooled Data from 5 Mills and 3 Instruments Over 3Seasons Calibration Statistics Validation Statistics StandardCorrelation Number of Standard Correlation Constituent ErrorCo-efficient Range (%) Samples Error Co-efficient Slope Fibre 0.5180.865 11.77-20.33 556 0.71 0.763 0.92 Pol in juice 0.444 0.96110.90-23.52 3089 0.48 0.969 1.03 Brix in juice 0.437 0.957 14.00-25.903103 0.45 0.941 1.01 Individual CCS 0.339 0.952  9.01-17.13 1169 0.330.957 1.01 Combustion Ash 0.443 0.782 0.36-6.97 1340 0.50 0.705 1.00 DryMatter 0.57 0.916 26.84-38.55 1217 0.61 0.906 1.02 Pol in open cells0.655 0.918 80.63-92.73 89 1.81 0.588 0.81 Phosphate in Juice (mg/L)5.40 0.925 23.79-126.1 92 20.56 0.85 0.99

TABLE 3 Global NIR Equation Statistics for Prepared Cane In Predictionat Mill 1 over 3 Seasons Using Instruments 1 and 2 Validation StatisticsStandard Correlation Range Number of Constituent Error Co-efficientSlope Bias (%) Samples Fibre 0.68 0.769 1.01 0.19 11.90-20.20 651 Pol injuice 0.48 0.958 1.04 0.12  9.20-25.90 14804 Brix in juice 0.47 0.9171.00 0.01 12.10-24.90 14830 Individual CCS 0.39 0.931 1.04 0.10 7.74-15.85 928 Combustlon Ash 0.52 0.672 1.05 0.00 0.78-6.90 805 DryMatter 0.65 0.879 1.00 −0.09  25.9-36.15 1010 Pol in open cells 1.810.588 0.81 −0.20 81.0-93.0 120

TABLE 4 Global NIR Equation Statistics for Prepared Cane in Predictionat Mill 2 Over a Six Week Period Using Instrument 1 ValidationStatistics Standard Correlation Range Number of Constituent ErrorCo-efficient Slope Bias (%) Samples Fibre 0.54 0.922 1.01 0.2412.62-16.92 29 Pol in juice 0.48 0.91 0.86 0.03 12.0-19.9 2154 Brix injuice 0.46 0.9 0.87 −0.03 14.8-22.2 2154 Individual CCS 0.34 0.865 0.880.21 10.34-13.57 53 Combustion Ash 0.54 0.833 1.11 −0.02 0.65-6.54 338Dry Matter 0.60 0.868 1.00 0.04 27.07-34.96 178

TABLE 5 Global NIR Equation Statistics for Prepared Cane in Predictionat Mill 3 Over a Six Week Period Using Instrument 2 ValidationStatistics Standard Correlation Range Number of Constituent ErrorCo-efficient Slope Bias (%) Samples Fibre 0.53 0.862 0.91 0.1013.38-20.23 104 Pol in juice 0.38 0.96 1.00 0.13 10.9-22.2 2024 Brix injuice 0.39 0.95 0.97 0.04 14.2-24.4 2024 Individual CCS 0.31 0.956 1.02−0.01  9.10-16.07 203 Combustion Ash 0.35 0.654 0.81 0.03 0.84-3.36 65Dry Matter 0.51 0.911 1.04 −0.01 28.59-36.83 152

TABLE 6 Global NIR Equation Statistics for Prepared Cane in Predictionat Mill 4 Over a Six Week Period Using Instrument 2 ValidationStatistics Standard Correlation Range Number of Constituent ErrorCo-efficient Slope Bias (%) Samples Fibre 0.71 0.62 1.06 −0.20  9.3-17.7116 Pol in juice 0.43 0.94 1.07 −0.04 11.44-22.0  1881 Brix in juice0.42 0.92 1.05 −0.03 14.85-24.9  1881 Individual CCS 0.36 0.906 1.020.05  9.73-14.47 116 Combustion Ash 0.44 0.688 0.75 −0.02 0.76-4.14 88Dry Matter 0.66 0.81 1.01 0.00 27.52-33.45 108

TABLE 7 Global NIR Equation Statistics for Prepared Cane in Predictionat Mill 5 Over A Six Week Period Using Instrument 3 ValidationStatistics Standard Correlation Range Number of Constituent ErrorCo-efficient Slope Bias (%) Samples Fibre 0.54 0.64 0.75 −0.05 13.0-16.68 114 Pol in juice 0.49 0.89 1.13 0.07 15.2-24.0 1396 Brix injuice 0.46 0.89 1.14 0.11 18.2-25.9 1396 Individual CCS 0.38 0.9 1.210.02 12.44-17.13 74 Combustion Ash 0.30 0.4 0.50 −0.02  0.7-2.22 115 DryMatter 0.49 0.873 0.99 0.02 30.84-36.60 67

TABLE 8 Calibration Statistics for Prepared Cane Constituents Not YetConsidered Global Calibration Statistics Standard Correlation RangeNumber of Constituent Error Co-efficient (%) Samples Nitrogen in Juice24.127 0.938 188.0-630.0 33 (mg/L) Potassium % cane 0.003 0.99 0.07-0.2227 Calcium % cane 0.001 0.997 0.01-0.04 27 Silica % cane 0.036 0.9980.21-3.40 27 Magnesium % cane 0.001 0.995 0.01-0.03 27 Insoluble Ash0.207 0.905 0.51-3.04 43 Soluble Ash 0.066 0.501 0.27-0.60 43

It will be appreciated that while the system and method according to theinvention has been,exemplified in relation to the processing of sugarcane, they are broadly applicable to any other plant derived material inaccordance with the first and second embodiments.

What is claimed is:
 1. A system for the on-line measurement of aparameter in processed sugar cane, the system comprising: (a) a scanninghead mounted adjacent a continuous stream of processed cane, thescanning head comprising a remote light source and reflected lightgathering and transmission apparatus; (b) a near infraredspectrophotometer which includes a monochromator for resolving thereflected light into light of a discrete wavelength; (c) a databasecontaining a reference calibration equation linking absorptioncharacteristics by wavelength and the quantified presence of theparameter of interest; and (d) a computer for measuring the parameter byapplication of the calibration equation to the obtained spectrum for asample and managing said system.
 2. The system according to claim 1,wherein said scanning head mounting includes vibration dampening.
 3. Thesystem according to claim 1, wherein said spectrophotometer is vibrationinsulated.
 4. The system according to claim 1, wherein saidspectrophotometer is temperature insulated.
 5. The system according toclaim 1, wherein said spectrophotometer is contained within an insulatedand air-conditioned chamber.
 6. The system according to claim 1, whereinsaid sensing head is remote from said spectrophotometer and is linkedthereto by a fiber optic cable.
 7. The system according to claim 1,wherein said database comprises a plurality of reference calibrationequations.
 8. The system according to claim 1, wherein said scanninghead is mounted adjacent a feed chute for crushing rollers.
 9. A networkcomprising a plurality of systems according to claim 1, saidspectrophotometers of said network being standardised to onespectrophotometer within said network which serves as a masterspectrophotometer.
 10. A method of on-line measurement of a parameter inprocessed sugar cane, the method comprising the steps of: (i) obtainingan infrared reflectance spectrum from a stream of said processed cane;(ii) applying an appropriate calibration equation to the spectrum toquantify the presence of the parameter of interest; and (iii)statistically validating the spectrum obtained as being represented bythe calibration equation.
 11. The method according to claim 10, whereinsaid parameter is selected from fiber content, juice brix, juicepolarisation, commercial cane sugar, quality parameters, inorganicelements, or process parameters.
 12. The method according to claim 10,wherein said processed sugar cane is selected from prepared cane,intermediate and final crushing roller bagasse, boiler feed materials,raw sugar, or crystalline sugar.
 13. The method according to claim 10,wherein said spectrum is a single spectrum or the average of a pluralityof obtained spectra.
 14. The method according to claim 10, wherein saidspectrum is measured over the range of 400 to 2,500 nm.
 15. The methodaccording to claim 10, wherein said infrared reflectance spectrum isobtained from a portion of the processed sugar cane passing through aby-pass line.
 16. The method according to claim 10, wherein saidinfrared reflectance spectrum is obtained from a stream set up foranalytical purposes.